Neuware - Learn methods of data analysis and their application to real-world data setsThis updated second edition serves as an introduction to data mining methods and models, including association rules, clustering, neural networks, logistic regression, and multivariate analysis. The authors apply a unified 'white box' approach to data mining methods and models. This approach is designed to walk readers through the operations and nuances of the various methods, using small data sets, so readers can gain an insight into the inner workings of the method under review. Chapters provide readers with hands-on analysis problems, representing an opportunity for readers to apply their newly-acquired data mining expertise to solving real problems using large, real-world data sets.Data Mining and Predictive Analytics, Second Edition:\* Offers comprehensive coverage of association rules, clustering, neural networks, logistic regression, multivariate analysis, and R statistical programming language\* Features over 750 chapter exercises, allowing readers to assess their understanding of the new material\* Provides a detailed case study that brings together the lessons learned in the book\* Includes access to the companion website, dataminingconsultant, with exclusive password-protected instructor contentData Mining and Predictive Analytics, Second Edition will appeal to computer science and statistic students, as well as students in MBA programs, and chief executives.

Bestandsnummer des Verk�ufers 9781118116197

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Bibliografische Details

Titel: Data Mining and Predictive Analytics
Verlag: Wiley & Sons, Wiley-IEEE Press
Erscheinungsjahr: 2015
Sprache: Englisch
ISBN-10: 1118116194
ISBN-13: 9781118116197
Einband: Buch
Auflage: 2. Auflage
Zustand: Neu

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Learn methods of data analysis and their application to real-world data sets

This updated second edition serves as an introduction to data mining methods and models, including association rules, clustering, neural networks, logistic regression, and multivariate analysis. The authors apply a unified “white box” approach to data mining methods and models. This approach is designed to walk readers through the operations and nuances of the various methods, using small data sets, so readers can gain an insight into the inner workings of the method under review. Chapters provide readers with hands-on analysis problems, representing an opportunity for readers to apply their newly-acquired data mining expertise to solving real problems using large, real-world data sets.

Data Mining and Predictive Analytics:

Offers comprehensive coverage of association rules, clustering, neural networks, logistic regression, multivariate analysis, and R statistical programming language
Features over 750 chapter exercises, allowing readers to assess their understanding of the new material
Provides a detailed case study that brings together the lessons learned in the book
Includes access to the companion website, www.dataminingconsultant, with exclusive password-protected instructor content

Data Mining and Predictive Analytics will appeal to computer science and statistic students, as well as students in MBA programs, and chief executives.

�ber die Autorin bzw. den Autor

Daniel T. Larose is Professor of Mathematical Sciences and Director of the Data Mining programs at Central Connecticut State University. He has published several books, including Data Mining the Web: Uncovering Patterns in Web Content, Structure, and Usage (Wiley, 2007) and Discovering Knowledge in Data: An Introduction to Data Mining (Wiley, 2005). In addition to his scholarly work, Dr. Larose is a consultant in data mining and statistical analysis working with many high profile clients, including Microsoft, Forbes Magazine, the CIT Group, KPMG International, Computer Associates, and Deloitte, Inc.

Chantal D. Larose is an Assistant Professor of Statistics & Data Science at Eastern Connecticut State University (ECSU). She has co-authored three books on data science and predictive analytics. She helped develop data science programs at ECSU and at SUNY New Paltz. She received her PhD in Statistics from the University of Connecticut, Storrs in 2015 (dissertation title: Model-based Clustering of Incomplete Data).

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